ArticleScience advances2025
Emergent social conventions and collective bias in LLM populations.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Large Language Models in Answering Dental Examination Questions: A Systematic Review and Meta-Analysis.International dental journal · 2026Pooled it
- Structural Divergence Between the Moltbook AI-Agent Network and Human Social Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Group size effects and collective misalignment in LLM multi-agent systems.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- AI agents can coordinate via majority-following beyond human scale.Science advances · 2026Article
- Understanding large language models demands distinguishing human projection from machine cognition.Communications psychology · 2026Review
- Evolvable AI: Threats of a new major transition in evolution.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A large-scale evaluation of commonsense knowledge in humans and large language models.PNAS nexus · 2026Article
- Unmasking conversational bias in AI multiagent systems.PloS one · 2026Article
- Why we believe chatbots: trust calibration as a design problem.Frontiers in psychology · 2026Article
- Network formation and dynamics among multi-LLMs.PNAS nexus · 2025Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Social conventions are the backbone of social coordination, shaping how individuals form a group. As growing populations of artificial intelligence (AI) agents communicate through natural language, a fundamental question is whether they can bootstrap the foundations of a society. Here, we present experimental results that demonstrate the spontaneous emergence of universally adopted social conventions in decentralized populations of large language model (LLM) agents. We then show how strong collective biases can emerge during this process, even when agents exhibit no bias individually. Last, we examine how committed minority groups of adversarial LLM agents can drive social change by imposing alternative social conventions on the larger population. Our results show that AI systems can autonomously develop social conventions without explicit programming and have implications for designing AI systems that align, and remain aligned, with human values and societal goals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.